Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Conventionally, EEG is treated as a multivariate temporal signal, where time- or frequency-domain features are extracted for representation learning. This paper investigates a simple yet effective EEG representation, i.e., microstates. Microstates represent the building blocks of brain activity patterns at a microscopic time scale. We build a universal microstate tokenizer from a large medical EEG dataset by clustering continuous EEG signals into sequences of discrete microstates. The microstate tokenizer is then adopted universally across a series of downstream tasks, including sleep staging, emotion recognition, and motor imagery classification. Experimental results show that EEG representation learning with microstates outperforms traditional time-domain and frequency-domain features under different models and across different tasks. Further analysis shows that microstates offer greater interpretability and scalability, thereby opening up applications in both cognitive neuroscience and clinical research.

翻译：从脑电图信号中学习通用表征是神经信息学和脑机接口领域的前沿方法。传统上，脑电图被视为多变量时间信号，通过提取时域或频域特征进行表征学习。本文研究了一种简单而有效的脑电图表征——微状态。微状态代表了在微观时间尺度上大脑活动模式的基本构建单元。我们通过将连续脑电图信号聚类为离散微状态序列，从大规模医学脑电图数据集中构建了一个通用微状态分词器。该微状态分词器随后被广泛用于一系列下游任务，包括睡眠分期、情绪识别和运动想象分类。实验结果表明，在不同模型和不同任务下，基于微状态的脑电图表征学习优于传统的时域和频域特征。进一步分析表明，微状态具有更强的可解释性和可扩展性，从而为认知神经科学和临床研究开辟了应用前景。